AI and agentic transformation
From AI possibility to agents that do real work
Enterprise AI strategy, agent architecture and agentic AI implementation with governance, evaluation and lifecycle operations built in.
The challenge
Most organisations have AI pilots. Few have agents running in production with clear owners, guardrails and a measured return. The gap is rarely the model — it's architecture, data access, governance and operations.
What you get
- A prioritised portfolio of AI and agent use cases with owners
- Agents in production that act within agreed permissions
- Evaluation and monitoring that show whether agents are working
- A repeatable path from idea to governed agent
Reference topology
Multi-agent system reference topology
Requests from Teams, Microsoft 365 Copilot, web apps and business events pass an AI gateway to an orchestrator agent, which plans and routes work to research, data, action and reviewer agents. Agents use enterprise knowledge, OneLake and business systems through MCP tools; consequential steps go to a human for approval; every run is traced and evaluated.
One request, many agents — every step traceable
The orchestrator plans, specialist agents work in parallel through governed tools, a reviewer agent checks quality and policy, and a person approves anything consequential. Each step is logged with its latency and cost.
- 01orchestratorplan: "approve supplier invoice #INV-2041" → 4 tasks
- 02research-agentretrieved 5 passages · contract + policy (AI Search)
- 03data-agentSQL on warehouse: PO match 3-way ✓
- 04action-agentMCP erp.create_payment_draft → draft saved
- 05reviewer-agentgroundedness 0.94 · policy check passed
- 06humanfinance approver accepted in Teams
- 07orchestratordone · 11.2 s · tokens logged to cost dashboard
What we deliver
Strategy and discovery
- Enterprise AI strategy
- AI readiness assessment
- AI opportunity discovery
Enterprise agent engineering
- Enterprise agent architecture
- Copilot Studio implementation
- Business workflow automation
- Knowledge integration
- Agent testing and evaluation
- Secure action execution
- Human approval workflows
- Agent lifecycle management
Agent governance and operations
- Agent inventory and ownership
- Identity and access governance
- Lifecycle controls
- Monitoring and observability
- Security and compliance
- Usage reporting
- Governance policies
- Operational readiness
Architecture layers
- Microsoft 365 Copilot
- Teams
- Web and line-of-business apps
- Copilot Studio agents
- Foundry Agent Service
- Human approval steps
- Copilot connectors
- SharePoint and Dataverse
- APIs and MCP tools
- Microsoft Entra identities
- Purview data controls
- Agent 365 inventory and monitoring
Technologies and availability
Microsoft Copilot Studio
Generally availableLow-code platform to build, test and publish agents.
Official source (opens in a new tab)Microsoft Foundry Agent Service
Generally availableSome sub-features remain in public preview. The classic agents experience retires on 31 March 2027.
Official source (opens in a new tab)Microsoft Agent 365
Generally availableControl plane to observe, govern and secure agents; generally available since 1 May 2026. Integrates with Microsoft Entra, Purview and Defender. Some newer capabilities ship through Frontier first.
Prerequisites: Separate licence or an including suite.
Official source (opens in a new tab)Microsoft 365 Copilot connectors
Generally availableFormerly Microsoft Graph connectors. Bring external line-of-business content into Microsoft Graph so Copilot can reason over it. Not available in DoD environments.
Official source (opens in a new tab)
Also used
- Model Context Protocol (MCP)
- Agent2Agent (A2A) patterns
Availability checked against official vendor sources on 11 October 2026. Features, licensing and regional availability change — we confirm them for your tenant during assessment.
How we work
Stage 1
Discover
Map candidate processes and score them on value, data readiness and risk.
Stage 2
Design
Define each agent's scope, knowledge, actions, guardrails and human hand-offs.
Stage 3
Build and evaluate
Build, test against evaluation sets and red-team before release.
Stage 4
Operate
Monitor behaviour, quality and cost; tune, extend or retire.
Security and governance, built in
- Every agent has a named business owner and a documented scope
- Agents act with least-privilege identities
- Sensitive data stays within labelled boundaries
- High-impact actions require human approval
- Behaviour and cost are logged and reviewed
Ways to engage
- Assessment
- A fixed-scope review with findings, priorities and a costed roadmap.
- Pilot
- A time-boxed build with agreed success criteria and a go/no-go decision.
- Delivery
- Full implementation by a Techcerv team working alongside yours.
- Managed
- Ongoing operation and improvement under a contracted service scope.
Build an enterprise AI agent
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